A multimodal deep fusion graph framework to detect social distancing violations and FCGs in pandemic surveillance

In pandemic surveillance, ensuring social distance has emerged as a challenging issue due to the lack of proper therapeutic agents, and this envisages the need for automated social distance monitoring to avoid the formation of social gatherings and free-standing conversation groups (FCGs). The robus...

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Vydané v:	Engineering applications of artificial intelligence Ročník 103; s. 104305
Hlavní autori:	Varghese, Elizabeth B., Thampi, Sabu M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.08.2021
Predmet:	Convolutional Variational Autoencoder (CVAE) Free-standing conversation group (FCG) Multimodal data fusion Pandemic surveillance Social distancing Pandemic surveillance Free-standing conversation group (FCG) Convolutional Variational Autoencoder (CVAE) Social distancing Multimodal data fusion
ISSN:	0952-1976, 1873-6769
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